
# import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sb
%matplotlib inline
import warnings
warnings.simplefilter("ignore")


#Load data
df_loan = pd.read_csv('prosperLoanData.csv')


# set maximum number of columns to be displayed to number of columns in dataset
pd.set_option('display.max_columns', df_loan.shape[1])


#load loan data to visually access it
df_loan


#Check top rows of the data
df_loan.head()


#Check number of rows and columns()
df_loan.shape

(113937, 81)


#Check data characteristics
df_loan.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113937 non-null  object 
 1   ListingNumber                        113937 non-null  int64  
 2   ListingCreationDate                  113937 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113937 non-null  int64  
 5   LoanStatus                           113937 non-null  object 
 6   ClosedDate                           55089 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113937 non-null  float64
 9   LenderYield                          113937 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113937 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113937 non-null  bool   
 22  CurrentlyInGroup                     113937 non-null  bool   
 23  GroupKey                             13341 non-null   object 
 24  DateCreditPulled                     113937 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113937 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113937 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105383 non-null  float64
 47  IncomeRange                          113937 non-null  object 
 48  IncomeVerifiable                     113937 non-null  bool   
 49  StatedMonthlyIncome                  113937 non-null  float64
 50  LoanKey                              113937 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113937 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113937 non-null  int64  
 62  LoanNumber                           113937 non-null  int64  
 63  LoanOriginalAmount                   113937 non-null  int64  
 64  LoanOriginationDate                  113937 non-null  object 
 65  LoanOriginationQuarter               113937 non-null  object 
 66  MemberKey                            113937 non-null  object 
 67  MonthlyLoanPayment                   113937 non-null  float64
 68  LP_CustomerPayments                  113937 non-null  float64
 69  LP_CustomerPrincipalPayments         113937 non-null  float64
 70  LP_InterestandFees                   113937 non-null  float64
 71  LP_ServiceFees                       113937 non-null  float64
 72  LP_CollectionFees                    113937 non-null  float64
 73  LP_GrossPrincipalLoss                113937 non-null  float64
 74  LP_NetPrincipalLoss                  113937 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64
 76  PercentFunded                        113937 non-null  float64
 77  Recommendations                      113937 non-null  int64  
 78  InvestmentFromFriendsCount           113937 non-null  int64  
 79  InvestmentFromFriendsAmount          113937 non-null  float64
 80  Investors                            113937 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.1+ MB


#Check data description
df_loan.describe()


#Check datatypes
df_loan.dtypes

ListingKey                      object
ListingNumber                    int64
ListingCreationDate             object
CreditGrade                     object
Term                             int64
                                ...   
PercentFunded                  float64
Recommendations                  int64
InvestmentFromFriendsCount       int64
InvestmentFromFriendsAmount    float64
Investors                        int64
Length: 81, dtype: object


#Check for null values
df_loan.isnull().sum()

ListingKey                         0
ListingNumber                      0
ListingCreationDate                0
CreditGrade                    84984
Term                               0
                               ...  
PercentFunded                      0
Recommendations                    0
InvestmentFromFriendsCount         0
InvestmentFromFriendsAmount        0
Investors                          0
Length: 81, dtype: int64


#Check for duplicates
df_loan.duplicated().sum()

0


# Visualise the relative proportions of each datatype to other datatypes
datatype = df_loan.dtypes.to_frame()
datatype.columns = ['dtype']
datatype['dtype'] = datatype['dtype'].astype('category')
datatype['dtype'] = datatype['dtype'].apply(lambda x: str(x))
datatype.T

# Calculate the relative proportions 
no_of_obsevation =  datatype['dtype'].shape[0]
max_proportion = datatype['dtype'].value_counts().to_list()[0]/no_of_obsevation
tick_proportions = np.arange(0, max_proportion, 0.03)
tick_names = ('{:0.2f}'.format(v) for v in tick_proportions)


plt.figure(figsize =(6,6))
order = datatype['dtype'].value_counts().index.to_list()
sb.countplot(x = "dtype" , data =datatype, order = order, color =sb.color_palette()[4],)
plt.xlabel('data types', fontdict = {'weight': 'bold'})
plt.ylabel('count of data types', fontdict = {'weight': 'bold'})
plt.yticks(tick_proportions * no_of_obsevation, tick_names);

#Calculate and display the percentage of each datatype
for i in range(datatype['dtype'].value_counts().shape[0]):
    count = datatype['dtype'].value_counts().to_list()[i]
    pct_string = '{:0.1f}%'.format(100*(count/no_of_obsevation))
    plt.text(i,count+1, pct_string, ha = 'center');


# make a copy of original data to clean
df_loan_copy =df_loan.copy()


df_loan_copy['ProsperRating (Alpha)'] = df_loan_copy[['CreditGrade', 'ProsperRating (Alpha)']]\
['ProsperRating (Alpha)'].fillna(df_loan_copy['CreditGrade'])
df_loan_copy['CreditGrade'] = df_loan_copy[['CreditGrade', 'ProsperRating (Alpha)']]['CreditGrade']\
.fillna(df_loan_copy['ProsperRating (Alpha)'])


df_loan_copy[['CreditGrade', 'ProsperRating (Alpha)']].head(5)


df_loan_copy['LoanOriginationQuarter'] = df_loan_copy['LoanOriginationQuarter'].apply(lambda x: x.split(' ')[0])


df_loan_copy['LoanOriginationQuarter'][1]

'Q1'


# ordinal categorical
ordinal_categorical = {'ProsperRating (Alpha)':['AA','A','B','C','D','E','HR'],
                       'CreditGrade': ['AA','A','B','C','D','E','HR'],
                       'IncomeRange': ['$0', '$1 - 24,999', '$25,000 - 49,999', '$50,000 - 74,999',\
                                     '$75,000 - 99,999', '$100,000+', 'Not employed', 'Not displayed'],
                       'LoanOriginationQuarter': ['Q1', 'Q2', 'Q3', 'Q4']
                      }


#nominal categorical
nominal_categorical = ['EmploymentStatus','LoanStatus', 'BorrowerState', 'Occupation']


for var in ordinal_categorical:
    ordered_var = pd.api.types.CategoricalDtype(ordered = True,
                                                categories = ordinal_categorical[var])
    df_loan_copy[var] = df_loan_copy[var].astype(ordered_var)
        
for var in nominal_categorical:
    df_loan_copy[var] = df_loan_copy[var].astype('category')


print(df_loan_copy[ordinal_categorical].dtypes, end = '\n\nnominal data \n')
print(df_loan_copy[nominal_categorical].dtypes)

ProsperRating (Alpha)     category
CreditGrade               category
IncomeRange               category
LoanOriginationQuarter    category
dtype: object

nominal data 
EmploymentStatus    category
LoanStatus          category
BorrowerState       category
Occupation          category
dtype: object


df_loan_copy =  df_loan_copy[['LoanStatus', 'LenderYield', 'ProsperRating (Alpha)', 'ListingCategory (numeric)', \
                        'BorrowerState', 'Occupation', 'EmploymentStatus', 'IsBorrowerHomeowner', \
                        'CurrentlyInGroup', 'CreditScoreRangeLower', 'CreditScoreRangeUpper', \
                        'TotalCreditLinespast7years', 'DebtToIncomeRatio', 'IncomeRange', \
                        'IncomeVerifiable', 'LoanOriginationQuarter', 'MonthlyLoanPayment', \
                        'Recommendations', 'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount',\
                        'Investors', 'PercentFunded']]


print(df_loan_copy.columns.to_list())
df_loan_copy.head()

['LoanStatus', 'LenderYield', 'ProsperRating (Alpha)', 'ListingCategory (numeric)', 'BorrowerState', 'Occupation', 'EmploymentStatus', 'IsBorrowerHomeowner', 'CurrentlyInGroup', 'CreditScoreRangeLower', 'CreditScoreRangeUpper', 'TotalCreditLinespast7years', 'DebtToIncomeRatio', 'IncomeRange', 'IncomeVerifiable', 'LoanOriginationQuarter', 'MonthlyLoanPayment', 'Recommendations', 'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount', 'Investors', 'PercentFunded']


df_loan_copy = df_loan_copy.dropna(subset = ['EmploymentStatus'])
df_loan_copy = df_loan_copy.dropna(subset = ['TotalCreditLinespast7years'])
df_loan_copy = df_loan_copy.dropna(subset = ['ProsperRating (Alpha)'])


#Check for null values
df_loan_copy.isnull().sum()

LoanStatus                         0
LenderYield                        0
ProsperRating (Alpha)              0
ListingCategory (numeric)          0
BorrowerState                   4084
Occupation                      1333
EmploymentStatus                   0
IsBorrowerHomeowner                0
CurrentlyInGroup                   0
CreditScoreRangeLower              0
CreditScoreRangeUpper              0
TotalCreditLinespast7years         0
DebtToIncomeRatio               8427
IncomeRange                    87303
IncomeVerifiable                   0
LoanOriginationQuarter             0
MonthlyLoanPayment                 0
Recommendations                    0
InvestmentFromFriendsCount         0
InvestmentFromFriendsAmount        0
Investors                          0
PercentFunded                      0
dtype: int64


df_loan_copy.drop(['IncomeRange'],  axis =1, inplace = True)


df_loan_copy.isnull().sum()

LoanStatus                        0
LenderYield                       0
ProsperRating (Alpha)             0
ListingCategory (numeric)         0
BorrowerState                  4084
Occupation                     1333
EmploymentStatus                  0
IsBorrowerHomeowner               0
CurrentlyInGroup                  0
CreditScoreRangeLower             0
CreditScoreRangeUpper             0
TotalCreditLinespast7years        0
DebtToIncomeRatio              8427
IncomeVerifiable                  0
LoanOriginationQuarter            0
MonthlyLoanPayment                0
Recommendations                   0
InvestmentFromFriendsCount        0
InvestmentFromFriendsAmount       0
Investors                         0
PercentFunded                     0
dtype: int64


df_loan_copy['Occupation'] = df_loan_copy['Occupation'].cat.add_categories('Not Stated')
df_loan_copy['Occupation'].fillna('Not Stated', inplace = True)


df_loan_copy['DebtToIncomeRatio'] = df_loan_copy['DebtToIncomeRatio'].interpolate()


df_loan_copy['BorrowerState'] = df_loan_copy['BorrowerState'].cat.add_categories('Not Stated')
df_loan_copy['BorrowerState'].fillna('Not Stated', inplace = True)


df_loan_copy.isnull().sum()

LoanStatus                     0
LenderYield                    0
ProsperRating (Alpha)          0
ListingCategory (numeric)      0
BorrowerState                  0
Occupation                     0
EmploymentStatus               0
IsBorrowerHomeowner            0
CurrentlyInGroup               0
CreditScoreRangeLower          0
CreditScoreRangeUpper          0
TotalCreditLinespast7years     0
DebtToIncomeRatio              0
IncomeVerifiable               0
LoanOriginationQuarter         0
MonthlyLoanPayment             0
Recommendations                0
InvestmentFromFriendsCount     0
InvestmentFromFriendsAmount    0
Investors                      0
PercentFunded                  0
dtype: int64


df_loan_copy['ListingCategory (numeric)'] = df_loan_copy['ListingCategory (numeric)'].replace(\
{0: 'Not AVilable', 1: 'Debt Consolidation', 2: 'HOme Improvement',3: 'Business', 4: 'Personal Loan',\
 5: 'Student Use', 6: 'Auto', 7: 'Other',  8: 'Baby & Adoption', 9: 'Boat', 10: 'Cosmetic Procedures',\
 11: 'Engagement Ring', 12: 'Green Loans', 13: 'Household Expenses', 14: 'Large Purchases',\
 15: 'Medical/ Dental', 16: 'Motorcycle', 17: 'RV', 18: 'Taxes', 19: 'Vacation', 20: 'Wedding Loans'})
df_loan_copy['ListingCategory (numeric)'] = df_loan_copy['ListingCategory (numeric)'].astype('category')


print(df_loan_copy['ListingCategory (numeric)'].dtype)
df_loan_copy['ListingCategory (numeric)'].value_counts()

category

Debt Consolidation     58242
Not AVilable           14572
Other                  10469
HOme Improvement        7421
Business                7172
Auto                    2564
Personal Loan           2392
Household Expenses      1996
Medical/ Dental         1522
Taxes                    885
Large Purchases          876
Wedding Loans            771
Vacation                 768
Student Use              750
Motorcycle               304
Engagement Ring          217
Baby & Adoption          199
Cosmetic Procedures       91
Boat                      85
Green Loans               59
RV                        52
Name: ListingCategory (numeric), dtype: int64


df_loan_copy.rename(columns = {'ListingCategory (numeric)': 'ListingCategory'}, inplace = True)


df_loan_copy.columns

Index(['LoanStatus', 'LenderYield', 'ProsperRating (Alpha)', 'ListingCategory',
       'BorrowerState', 'Occupation', 'EmploymentStatus',
       'IsBorrowerHomeowner', 'CurrentlyInGroup', 'CreditScoreRangeLower',
       'CreditScoreRangeUpper', 'TotalCreditLinespast7years',
       'DebtToIncomeRatio', 'IncomeVerifiable', 'LoanOriginationQuarter',
       'MonthlyLoanPayment', 'Recommendations', 'InvestmentFromFriendsCount',
       'InvestmentFromFriendsAmount', 'Investors', 'PercentFunded'],
      dtype='object')


df_loan_copy.to_csv('cleaned_Loan_prosper_data.csv')


#Top 10 states by distribution of borrowers
plt.figure(figsize = [12, 8])
color_pal = sb.color_palette()[5]
order = df_loan_copy['BorrowerState'].value_counts().iloc[:10].index
sb.countplot(data= df_loan_copy, y= 'BorrowerState', color = color_pal, order = order)
plt.title("Distribution of Borrowers in Top States",fontdict = {'weight': 'bold', 'fontsize':16})
plt.xlabel("Frequency", fontdict = {'weight': 'bold', 'fontsize':16})
plt.ylabel("States", fontdict = {'weight': 'bold', 'fontsize':16})

Text(0, 0.5, 'States')


#Distribution of the borrowers across the EmploymentStatus categories
plt.figure(figsize = [12, 8])
color_pal = sb.color_palette()[5]
order_type = df_loan_copy['EmploymentStatus'].value_counts().index
sb.countplot(data= df_loan_copy, y= 'EmploymentStatus', color = color_pal, order = order_type)
plt.title("Distribution of Borrowers across Employment Status",fontdict = {'weight': 'bold', 'fontsize':16})
plt.xlabel("Frequency",fontdict = {'weight': 'bold', 'fontsize':14})
plt.ylabel("Emloyment status",fontdict = {'weight': 'bold', 'fontsize':14});


#Occupation of borrowers
plt.figure(figsize = [12, 8])
color_pal = sb.color_palette()[5]
order = df_loan_copy['Occupation'].value_counts().iloc[:10].index
sb.countplot(data= df_loan_copy, y= 'Occupation', color = color_pal, order = order)
plt.title("Distribution of Borrowers across Top Occupations",fontdict = {'weight': 'bold', 'fontsize':16})
plt.xlabel("Frequency",fontdict = {'weight': 'bold', 'fontsize':14})
plt.ylabel("Occupation",fontdict = {'weight': 'bold', 'fontsize':14})

Text(0, 0.5, 'Occupation')


def distribution (data, onx, sides_to_remove, title, fig_width, fig_height, rotation,\
          sort_as_ordinal = None, rotatex = None, subplot = None):
    value = df_loan_copy[data].value_counts().index.to_list() if sort_as_ordinal == None else\
     df_loan_copy[data].value_counts().sort_index().index.to_list()
    fig = plt.figure(figsize = (fig_width, fig_height))
    ax1 = plt.subplot(111) if subplot == None else plt.subplot(subplot)
    ax1.set_title(title, fontdict = {'color':'brown', 'fontsize':20,\
                                                    'fontweight': 'bold'}, pad = 10)
    if onx:
        #if the categorical variale should be on x-axis
        sb.countplot(x = data, data = df_loan_copy, order = value, color = 'brown', ax = ax1)
        for i in range(len(value)):
            distribution = df_loan_copy[data].value_counts()[value[i]]
            percent = (distribution/df_loan_copy[data].shape[0]) * 100
            plt.text(x = i, y = distribution , s = '{:0.0f}% ({c})'.format(percent, c = distribution), rotation = 45 if\
                     rotation else 0, fontsize = 16, ha ='center') # to print the proportions in percentage 
        plt.xlabel(f'{data}', fontdict = {'weight': 'bold', 'size' : 16})
        plt.ylabel(f'Frequency of {data}', fontdict = {'weight': 'bold', 'size' : 16})
        plt.xticks(fontsize=16, rotation = 0 if rotatex == False else 90);
        plt.yticks(fontsize=16)
    else:
        sb.countplot(y = data, data = df_loan_copy, order = value, color = 'brown', ax = ax1)
        plt.ylabel(f'{data}', fontdict = {'weight': 'bold', 'size' : 16})
        plt.xlabel(f'Frequency of {data}', fontdict = {'weight': 'bold', 'size' : 16})
        for i in range(len(value)):
            distribution = df_loan_copy[data].value_counts()[value[i]]
            percent = (distribution/df_loan_copy[data].shape[0]) * 100
            plt.text(y = i, x = distribution , s = '{:0.0f}% ({c})'.format(percent, c = distribution), rotation = 45 if\
                     rotation else 0, fontsize = 16, ha = 'left')
    for i in sides_to_remove:
        ax1.spines[i].set_visible(False)


#Distribution of ProsperRating (Alpha)

sides = ['top', 'right']
distribution(data = 'ProsperRating (Alpha)', onx = False, sides_to_remove = sides,\
      title ='Distribution of ProsperRating (Alpha)', fig_width = 20, fig_height = 8, rotation = False)


#Distribution of ListingCategory
sides = ['top', 'right']
distribution(data = 'ListingCategory', onx = False, sides_to_remove = sides,\
      title ='Distribution of Listing Category', fig_width = 10, fig_height = 10, rotation = False)


#Distribution of Loan Status

sides = ['right', 'top']
distribution(data = 'LoanStatus', onx = True, sides_to_remove = sides, title ='Distribution of Loan Status',\
     fig_width = 20, fig_height = 10, rotation = True)


##plot of LoanOriginationQuarter
sides = ['right', 'top']
distribution(data = 'LoanOriginationQuarter', onx = True, sides_to_remove = sides, title ='Distribution across Quater',\
     fig_width = 20, fig_height = 10, rotation = True, sort_as_ordinal = True, rotatex = False)


distribution(data = 'Recommendations', onx = False, sides_to_remove = ['top'], title ='Distribution of Recommendations',\
     fig_width = 20, fig_height = 10, rotation = False, sort_as_ordinal = True, rotatex = False)


f, ax = plt.subplots(2, 2, figsize = (10,10))
color = ['grey', 'brown']
label = [ 'False','True']

def proportion_pie(data, axis_row, axis_column, title, explode, rotation1, rotate_index, wedgeprops = None,):
    
    '''To plot distributions of categorical data in form of proportion
    
    data: str, The elements of the categorical data,
    axis_row: int, row position to plot on,
    axis_column: int, column position to plot on,
    title: str, title of plot,
    explode: list, explode values  to pass to pie plot,
    rotation1: bool, If all texts should be rotated,
    rotate_index: int, The index of the text to rotate if rotation1 is false,
    wedgeprops: dict, The width of each proportion, to help form a donut chart
    '''
    
    data = df_loan_copy[data].value_counts().sort_index()
    axes = ax[axis_row][axis_column]
    axes.set_title(title, backgroundcolor = 'brown',\
         fontdict = {'fontweight': 'bold', 'color': 'white'}, pad = 71)
    explode = explode
    if wedgeprops != None:
        patches, texts, autotexts = axes.pie(data, colors = color, autopct = '%0.0f%%', startangle = 87,
        explode = explode, wedgeprops = wedgeprops,\
            textprops = {'color': 'white', 'fontweight' : 'bold','fontsize':16},  radius = 2);
    else:
        patches, texts, autotexts = axes.pie(data, colors = color, autopct = '%0.0f%%', startangle = 87,
        explode = explode, textprops = {'color': 'white', 'fontweight' : 'bold','fontsize':16},  radius = 2);
    if rotation1:
        [text.set_rotation(0) for text in autotexts] 
    else:
        autotexts[rotate_index].set_rotation(0)
    legend_properties = {'weight': 'bold'}
    f.legend(label, loc = 4 , facecolor = 'teal',borderpad = 4, labelspacing = 4,\
            fontsize = 16, prop = legend_properties, labelcolor = 'white',\
            bbox_to_anchor = (0, 0.5));
    plt.subplots_adjust(wspace = 0.9, hspace = 0.9)
    ax[1][1].set_visible(False)
    return axes

#percentage of IsBorrowerHomeowner
proportion_pie('IsBorrowerHomeowner', 0, 0, 'Percentage of Borrowers that are home owners', [0.07, 0],\
   True,{'width': 1.0})

#percentage of CurrentlyInGroup
proportion_pie('CurrentlyInGroup', 0, 1, 'Percentage of Borrowers that are in a group', [0, 0.07],\
              False, 1)

#percentage of IncomeVerifiable
proportion_pie('IncomeVerifiable', 1, 0, 'Percentage of verifiable Income', [0, 0.07],\
              False, 0)

plt.show()


# Histogram showing the distribution of the Borrowers DebtToIncomeRatio
plt.figure(figsize = [12, 8])
bin= np.arange(0, df_loan_copy['DebtToIncomeRatio'].max()+0.05, 0.05)
plt.hist(data= df_loan_copy, x = 'DebtToIncomeRatio', bins= bin)
plt.xlim(0, 1)
plt.xlabel('Debt To Income Ratio')

Text(0.5, 0, 'Debt To Income Ratio')


# using subplots to create Histograms showing the distribution of the Credit Score Upper and lower Range
fig, ax = plt.subplots(nrows=2, figsize = [12,8])
variables = ['CreditScoreRangeUpper', 'CreditScoreRangeLower']
for i in range(len(variables)):
    var = variables[i]
    bins = np.arange(min(df_loan_copy[var]), max(df_loan_copy[var])+20, 20)
    ax[i].hist(data = df_loan_copy, x = var, bins = bins)
    ax[i].set_xlabel('{}'.format(var))
    ax[i].set_xlim(400, 900)

plt.show()


def hist(fig_width, fig_height, data, number_of_bins, title, more_than_a_subplot = False, \
               use_another_data = False, another_data = None,):
    
    plt.figure(figsize = (fig_width, fig_height))
    ax = plt.subplot(121)
    data_to_use = df_loan_copy
    bin_number = data_to_use[data[0]].max() / number_of_bins
    bins = np.arange(data_to_use[data[0]].min(), data_to_use[data[0]].max() +bin_number, bin_number)
    sb.histplot(x = data[0], data = data_to_use, ax = ax, bins = bins)
    ax.spines['top'].set_visible(False)
    plt.title(title if isinstance(title, str) else title[0], 
              fontdict = {'fontsize': 20})
    plt.xlabel(f'{data[0]}', fontdict = {'weight': 'bold', 'fontsize':16})
    plt.ylabel(f'frequency of {data[0]}', fontdict = {'weight': 'bold','fontsize':16})
    
    if more_than_a_subplot:
        ax = plt.subplot(122)
        if use_another_data:
            data_to_use = another_data
        else:
            data_to_use = data_copy
        bin_number = data_to_use[data[1]].max() / number_of_bins
        bins = np.arange(data_to_use[data[1]].min(), data_to_use[data[1]].max() +bin_number, bin_number)
        sb.histplot(x = data[1], data = data_to_use, ax = ax, bins = bins)
        ax.spines['top'].set_visible(False)
        plt.title(title[1], fontdict = {'fontsize': 40})
        plt.xlabel(f'{data[1]}', fontdict = {'weight': 'bold','fontsize':16})
        plt.ylabel(f'frequency of {data[1]}', fontdict = {'weight': 'bold','fontsize':16})
    plt.subplots_adjust(wspace = 0.9)
    plt.tight_layout()


# Histogram showing the distribution of MonthlyLoanPayment
hist(fig_width = 20, fig_height = 6, data = ['MonthlyLoanPayment'],\
           number_of_bins = 27, title = 'Distribution of MonthlyLoanPayment',\
                         )


# Histogram showing the distribution of Lender Yield
hist(fig_width = 20, fig_height = 6, data = ['LenderYield'],\
           number_of_bins = 27, title = 'Distribution of LenderYield',\
                         )


# Histogram showing the distribution of TotalCreditLinespast7years

hist(fig_width = 20, fig_height = 5, data = ['TotalCreditLinespast7years'],\
           number_of_bins = 27, title = 'Distribution of Total Credit Lines in past 7 years',\
                         )


plt.figure(figsize = (20,8))

plt.subplot(121)
sb.kdeplot(data = df_loan_copy['PercentFunded'], color = 'brown')
plt.title('Distribution of all PercentFunded', fontdict = {'fontsize': 18, 'color': 'black'}, pad = 0.4)
plt.xlabel('Percent Funded', fontdict = {'weight': 'bold'})
plt.ylabel('counts', fontdict = {'weight': 'bold'})
plt.ylim(-4);

#Because the proportion of 1.0 is very large, I want to view the distribution of other percent funded in another subplot
plt.subplot(122)
not_including_1 = df_loan_copy[df_loan_copy['PercentFunded'] < 1]
bins = np.arange(not_including_1['PercentFunded'].min(), not_including_1['PercentFunded'].max() + 0.025, 0.025)
sb.histplot(data =  not_including_1, x = 'PercentFunded', bins = bins, color = 'aqua')
plt.title('Distribution of PercentFunded excluding 1.0', fontdict = {'fontsize': 18, 'color': 'black'}, pad = 0.4)
plt.xlim(0.7,0.99,0.25)
plt.xlabel('Percent Funded', fontdict = {'weight': 'bold'})
plt.ylabel('Density', fontdict = {'weight': 'bold'})

plt.subplots_adjust(wspace = 1.0)


#check the percentage of loans that has PercentFunded of 1

(df_loan_copy['PercentFunded'].value_counts().iloc[0]/ df_loan_copy['PercentFunded'].shape[0]) * 100

99.2199772007145


#check for columns that have high positive correlation
a = df_loan_copy.corr()[df_loan_copy.corr() > 0.4]
sb.heatmap(a)
plt.title('Positive correlation between features greater than 0.4', fontdict = {'weight': 'bold'})

Text(0.5, 1.0, 'Positive correlation between features greater than 0.4')


#check for columns that have high negative correlation

a = df_loan_copy.corr()[df_loan_copy.corr() < -0.4]
sb.heatmap(a)

plt.title('Negative correlation between features less than 0.4', fontdict = {'weight': 'bold'})

Text(0.5, 1.0, 'Negative correlation between features less than 0.4')


#create a pairplot to show correlation with percentFunded

plt.figure(figsize = (10,10))
g = sb.pairplot(data = df_loan_copy, y_vars = 'PercentFunded')
plt.title('Pairplot of other features against Percent Funded to confirm', fontdict ={'weight': 'bold'})

Text(0.5, 1.0, 'Pairplot of other features against Percent Funded to confirm')

<Figure size 720x720 with 0 Axes>


def boxplots(fig_width, fig_height,  cat_data, num_data, data, title,
            multiple_subplots = False, use_another_data = False, another_data = None):
    
    plt.figure(figsize = (fig_width, fig_height))
    plt.subplot(211)
    sb.boxplot(x = cat_data[0], y = num_data[0], data = df_loan_copy, color = 'teal')
    plt.xticks(rotation = 90)
    plt.title(title[0], fontdict = {'fontweight': 'bold', 'fontsize' : 24})
    
    if multiple_subplots: #If there should be another subplot
        plt.subplot(212)
        if use_another_data:#If another data should be used
            data_to_use = another_data
        else:
            data_to_use = data
        sb.boxplot(x = cat_data[0], y = num_data[0], data = data_to_use, color = 'teal')
        plt.title(title[1], fontdict = {'fontweight': 'bold', 'fontsize' : 24})
        plt.xticks(rotation = 90);
        plt.subplots_adjust(hspace = 1.4)
    plt.xlabel(f'{cat_data[0]}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'{num_data[0]}', fontdict = {'weight': 'bold'})


def full_funding(cat_data, title, fig_size = None):
    
    if fig_size != None:
        plt.figure(figsize = fig_size)
    full_funding = df_loan_copy[df_loan_copy['PercentFunded'] == 1]# data of 100% funding
    sb.countplot(x = cat_data, data = full_funding, color = 'brown')
    plt.xticks(rotation = 90);
    plt.title(f'Counts of {title} that had full percent funding', 
             fontdict = {'fontweight': 'bold', 'fontsize' : 24});
    plt.xlabel(f'{cat_data}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'Counts of {cat_data[0]} for loans with full funding', fontdict = {'weight': 'bold'})


#Relationship between percentFunded and Loanstatus

not_including_1 = df_loan_copy[df_loan_copy['PercentFunded'] < 1]
boxplots(10, 10, ['LoanStatus'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and Loanstatus', 
          'Relationship between percentFunded and Loanstatus excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and Loanstatus for fully funded loans

full_funding('LoanStatus', 'loan status')


#Relationship between percentFunded and Loanstatus ProsperRating (Alpha)

not_including_1 = df_loan_copy[df_loan_copy['PercentFunded'] < 1]
boxplots(10, 10, ['ProsperRating (Alpha)'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and ProsperRating', 
          'Relationship between percentFunded and ProsperRating excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and ProsperRating (Alpha) for fully funded loans

full_funding('ProsperRating (Alpha)', 'ProsperRating')


#Relationship between percentFunded and ListingCategory

not_including_1 = df_loan_copy[df_loan_copy['PercentFunded'] < 1]
boxplots(10, 10, ['ListingCategory'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and ListingCategory', 
          'Relationship between percentFunded and ListingCategory excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and ListingCategory for fully funded loans

full_funding('ListingCategory', 'Listing Category', (10,4))


#Relationship between percentFunded and BorrowerState

not_including_1 = df_loan_copy[df_loan_copy['PercentFunded'] < 1]
boxplots(20, 10, ['BorrowerState'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and Borrower State', 
          'Relationship between percentFunded and Borrower State excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and BorrowerState for fully funded loans

full_funding('BorrowerState', 'States', (15, 5))


#Relationship between percentFunded and Occupation

boxplots(20, 10, ['Occupation'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and Occupation', 
          'Relationship between percentFunded and Occupation excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and Occupation for fully funded loans

full_funding('Occupation', 'Occupation', (15,8))


#Relationship between percentFunded and EmploymentStatus

boxplots(20, 10, ['EmploymentStatus'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and Employment Status', 
          'Relationship between percentFunded and Employment Status excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and EmploymentStatus for fully funded loans

full_funding('EmploymentStatus', 'EmploymentStatus')


#Relationship between percentFunded and IsBorrowerHomeowner 

boxplots(20, 10, ['IsBorrowerHomeowner'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and IsBorrowerHomeowner', 
          'Relationship between percentFunded and IsBorrowerHomeowner excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and IsBorrowerHomeowner for fully funded loans

full_funding('IsBorrowerHomeowner', 'Homeowner')


#Relationship between percentFunded and CurrentlyInGroup

boxplots(20, 10, ['CurrentlyInGroup'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and CurrentlyInGroup', 
          'Relationship between percentFunded and CurrentlyInGroup excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and CurrentlyInGroup for fully funded loans

full_funding('CurrentlyInGroup', 'borrowers in groups')


#Relationship between percentFunded and LoanOriginationQuarter

boxplots(20, 10, ['LoanOriginationQuarter'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and LoanOriginationQuarter', 
          'Relationship between percentFunded and LoanOriginationQuarter excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and LoanOriginationQuarter for fully funded loans

full_funding('LoanOriginationQuarter', 'Quarter that had loans')


#Relationship between percentFunded and IncomeVerifiable

boxplots(20, 10, ['IncomeVerifiable'], ['PercentFunded'], df_loan_copy,
         ['Relationship between percentFunded and IncomeVerifiable', 
          'Relationship between percentFunded and IncomeVerifiable excluding 1'],
          multiple_subplots = True, \
         use_another_data = True, another_data = not_including_1)


#Relationship between percentFunded and IncomeVerifiable for fully funded loans

full_funding('IncomeVerifiable', 'verified income')


def heatmap(x_data, y_data, number_of_bins, fig_size,more_subplot = False, x_data2 = None):

    
    plt.figure(figsize = fig_size)
    plt.subplot(211) if more_subplot else plt.subplot(111)
    no_of_bins_x = not_including_1[x_data].max()/ number_of_bins
    no_of_bins_y = not_including_1[y_data].max()/ number_of_bins
    bins_x = np.arange(not_including_1[x_data].min(), 
                 not_including_1[x_data].max() +  no_of_bins_x, no_of_bins_x)
    bins_y = np.arange(not_including_1[y_data].min(), 
                 not_including_1[y_data].max() +  no_of_bins_y, no_of_bins_y)
    plt.hist2d(x = x_data, y = y_data, data = not_including_1,
           bins = [bins_x, bins_y], cmap = 'viridis_r', )
    plt.title(f'Relationship between percentFunded and {x_data}')
    plt.xlabel(f'{x_data}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'{y_data}', fontdict = {'weight': 'bold'})
    
    if more_subplot:
        plt.subplot(212)
        no_of_bins_x = not_including_1[x_data2].max()/ number_of_bins
        bins_x = np.arange(not_including_1[x_data2].min(), 
                 not_including_1[x_data2].max() +  no_of_bins_x, no_of_bins_x)
        plt.hist2d(x = x_data2, y = y_data, data = not_including_1,
           bins = [bins_x, bins_y], cmap = 'viridis_r', )
        plt.title(f'Relationship between percentFunded and {x_data2}')
        plt.xlabel(f'{x_data2}', fontdict = {'weight': 'bold'})
        plt.ylabel(f'{y_data}', fontdict = {'weight': 'bold'})
    
    plt.subplots_adjust(hspace = 0.9)
    plt.colorbar();


def contvcont(x_data, y_data, number_of_bins, fig_size,more_subplot = False, x_data2 = None):
        
    plt.figure(figsize = fig_size)
    plt.subplot(211) if more_subplot else plt.subplot(111)
    no_of_bins_x = not_including_1[x_data].max()/ number_of_bins
    no_of_bins_y = not_including_1[y_data].max()/ number_of_bins
    bins_x = np.arange(not_including_1[x_data].min(), 
                 not_including_1[x_data].max() +  no_of_bins_x, no_of_bins_x)
    bins_y = np.arange(not_including_1[y_data].min(), 
                 not_including_1[y_data].max() +  no_of_bins_y, no_of_bins_y)
    sb.regplot(x = x_data, y = y_data, data = not_including_1,)
    plt.title(f'Relationship between percentFunded and {x_data}')
    plt.xlabel(f'{x_data}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'{y_data}', fontdict = {'weight': 'bold'})
    
    if more_subplot:
        plt.subplot(212)
        no_of_bins_x = not_including_1[x_data2].max()/ number_of_bins
        bins_x = np.arange(not_including_1[x_data2].min(), 
                 not_including_1[x_data2].max() +  no_of_bins_x, no_of_bins_x)
        sb.regplot(x = x_data, y = y_data, data = not_including_1)
        plt.title(f'Relationship between percentFunded and {x_data2}')
        plt.xlabel(f'{x_data}', fontdict = {'weight': 'bold'})
        plt.ylabel(f'{y_data}', fontdict = {'weight': 'bold'})
    
    plt.subplots_adjust(hspace = 0.9)


def full_fund_hist(data, number_of_bins, fig_size,more_subplot = False, data2 = None, log_plot = False):
   
    full_funding = df_loan_copy[df_loan_copy['PercentFunded'] == 1]
    plt.figure(figsize = fig_size) 
    plt.subplot(121) if more_subplot else plt.subplot(111)
    no_of_bins = full_funding[data].max()/ number_of_bins
    bins = np.arange(full_funding[data].min(), 
                 full_funding[data].max() +  no_of_bins, no_of_bins)
    sb.histplot(x = data, data = full_funding, bins = bins, stat = 'density')
    plt.title(f'{data} with full percentFunded')
    plt.xlabel(f'{data}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'frequency of {data} for fully funded loans', fontdict = {'weight': 'bold'})
    
    if more_subplot:
        plt.subplot(122)
        if log_plot:
            no_of_bins = np.log(full_funding[data] + 1).max()/ number_of_bins
            bins = np.arange(np.log(full_funding[data] + 1).min(), 
                 np.log(full_funding[data] + 1).max() +  no_of_bins, no_of_bins)
            plt.xscale('log')
            plt.xlim(0.9e0, 2e1)
            plt.xticks([0.9e0, 2e0, 4e0, 6e0, 8e0, 1e1], ['0', '2', '4', '6', '8', '10'])
        else:
            no_of_bins = full_funding[data2].max()/ number_of_bins
            bins = np.arange(full_funding[data2].min(), 
                 full_funding[data2].max() +  no_of_bins, no_of_bins)
        sb.histplot(x = data if log_plot else data2
                    , data = full_funding, bins = bins, stat = 'density')
        plt.xlabel(f'{data}', fontdict = {'weight': 'bold'})
        plt.ylabel(f'frequency of {data} for fully funded loans', fontdict = {'weight': 'bold'})
        
        if log_plot:
            plt.title(f'logarithm plot of {data} with full percentFunded')
        else:
            plt.title(f'logarithm plot of {data2} with full percentFunded')
    plt.subplots_adjust(wspace = 0.9)


contvcont('CreditScoreRangeLower', 'PercentFunded', 50, (10,10),more_subplot = True, x_data2 = 'CreditScoreRangeUpper')


full_fund_hist('CreditScoreRangeLower', 50, (10,10),more_subplot = True, data2 = 'CreditScoreRangeUpper')


heatmap('TotalCreditLinespast7years', 'PercentFunded', 50, (15, 6))


full_fund_hist('TotalCreditLinespast7years', 50, (10,6))


full_fund_hist('DebtToIncomeRatio', 50, (10,6), more_subplot = True, log_plot = True)


contvcont('DebtToIncomeRatio', 'PercentFunded', 50, (10,8),\
          more_subplot = True, x_data2 = 'CreditScoreRangeUpper')


def correlation(fig_size, data, multiple_subplot =  False):
    
    plt.figure(figsize = fig_size)
    plt.subplot(121) if multiple_subplot else plt.subplots(111)
    sb.regplot(x = data, y = 'PercentFunded', data = df_loan_copy)
    plt.title(f'Relationship between PercentFunded \nand {data}')
    plt.xlabel(f'{data}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'PercentFunded', fontdict = {'weight': 'bold'})
    if multiple_subplot:
        plt.subplot(122)
        sb.regplot(x = data, y = 'PercentFunded', data = not_including_1)
        plt.title(f'Relationship between PercentFunded \nand {data} of loans'
                  'that were not fully funded')
        plt.subplots_adjust(wspace  = 0.9)
        plt.xlabel(f'{data}', fontdict = {'weight': 'bold'})
        plt.ylabel(f'PercentFunded', fontdict = {'weight': 'bold'})


def fully_funded_for_numeric(fig_size, column):
    
    plt.figure(figsize = fig_size)
    plt.subplot(111)
    data_to_use = df_loan_copy[df_loan_copy['PercentFunded'] == 1]
    no_of_bins = data_to_use[column].max()/ 50
    bins = np.arange(data_to_use[column].min(), 
                 data_to_use[column].max() +  no_of_bins, no_of_bins)
    sb.histplot(x = column, data = data_to_use, bins = bins)
    plt.xlabel(f'{column}', fontdict = {'weight': 'bold'})
    plt.ylabel(f'Frequency of {column} for fully funded loans', fontdict = {'weight': 'bold'})


fully_funded_for_numeric((10,4), 'DebtToIncomeRatio')


correlation((10, 8), 'MonthlyLoanPayment', multiple_subplot = True)


correlation((10, 8), 'Recommendations', multiple_subplot = True)


def recommendations(column):
    
    '''To plot distribution of numeric data on recommendations for loans not fully funded
    
   column: str, The numeric data to plot, 
    '''
    
    fig, ax = plt.subplots(1,2)
    reco = not_including_1['Recommendations'].unique()
    for i, ax in zip(reco, ax.ravel()):
        no_recommendation = not_including_1[not_including_1['Recommendations'] == i]
        sb.boxplot(x = 'Recommendations', y = column, data = no_recommendation, ax = ax)
        plt.text(x = 1, y = 0.8, s = 'The only number of recommendation of loans that do not have '
                 'full funding are 0 and 1')
        ax.set_xlabel('Recommendation', fontdict = {'weight': 'bold'})
        ax.set_ylabel(f'PercentFunded', fontdict = {'weight': 'bold'})
    plt.title(f'Relationship between Recomendations and {column} of not fully funded loans',
                 fontdict = {'weight':'bold'})
    plt.subplots_adjust(wspace = 0.9)


recommendations('PercentFunded')


recommendations('InvestmentFromFriendsCount')


full_funded = df_loan_copy[df_loan_copy['PercentFunded'] == 1]
ax = plt.subplot(111)
prop = full_funded['Recommendations'].value_counts() / full_funded['Recommendations'].shape[0]
label = [str(x) for x in full_funded['Recommendations'].value_counts().index.to_list()]
sb.barplot(x = prop, y = label, ax  = ax)
for i in range(len(label)):
    prop = full_funded['Recommendations'].value_counts().iloc[i] / \
    full_funded['Recommendations'].shape[0]
    percent = prop * 100
    value = full_funded['Recommendations'].value_counts().iloc[i]
    plt.text(y = i, x = prop, s = '{:0.0f}% ,{}'.format(percent, value))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.title('Proportion of Recommendations for fully funded loans', fontdict = {'Weight': 'bold'},
          pad = 7)
plt.xlabel('proportion', fontdict = {'weight':'bold'})
plt.ylabel('Recommendations', fontdict = {'weight':'bold'});


correlation((10, 8), 'InvestmentFromFriendsAmount', multiple_subplot = True)


def relation(x_data, y_data, data):
    
    '''To plot relationship between numeric data
    
   x_data: str, The numeric data to plot on x-axis, 
   y_data: str, The numeric data to plot on y-axis, 
   data: DataFrame, The data to plot from
    '''
    
    sb.regplot(x = x_data, y = y_data, data = data);
    plt.title(f'relationship between {x_data} and {y_data}', fontdict = {'weight':'bold'}, pad = 7)
    plt.xlabel(f'{x_data}', fontdict = {'weight':'bold'})
    plt.ylabel(f'{y_data}', fontdict = {'weight':'bold'});


relation('InvestmentFromFriendsCount', 'Investors', df_loan_copy)


relation('Recommendations', 'InvestmentFromFriendsCount', df_loan_copy)


relation('LenderYield', 'PercentFunded', not_including_1)


def cat_rel(column, hue):
    
    '''To plot counts of two categorical varable
    
   x_data: str, The numeric data to plot on x-axis, 
   y_data: str, The numeric data to plot on y-axis, 
   data: DataFrame, The data to plot from
    '''
    
    sb.countplot(x = column, hue = hue, data = df_loan_copy)
    plt.xticks(rotation = 90)
    plt.title(f'Proportion of {column} and {hue}', fontdict = {'weight':'bold'}, pad = 7)
    plt.xlabel(f'{column}', fontdict = {'weight':'bold'})
    plt.ylabel(f'counts of {column} and {hue}', fontdict = {'weight':'bold'});


cat_rel('EmploymentStatus', 'IncomeVerifiable',)


cat_rel('ProsperRating (Alpha)', 'CurrentlyInGroup',)


def vari(col = None, numerical = False, facet = False, minimum = None, pad = None):
    
    '''To plot relationship between LenderYield, PercentFunded and one categorical variable or
     numerical variable
    
    col: str, categorical variable to plot, 
    numerical: str, numerical variable to plot, 
    facet: bool, if it should be facet plot, 
    minimum: int, minimum value for colorbar,
    pad = The pad for title
    '''
    
    plt.figure(figsize = (10,5))
    if numerical:
        plt.scatter(data = not_including_1, x = 'LenderYield', y = 'PercentFunded', c = col, \
           cmap = 'Accent', vmin = minimum)
        plt.colorbar(label = col)
    elif facet:
        g = sb.FacetGrid(data = not_including_1, col = col, col_wrap =3)
        g.map(sb.regplot, 'LenderYield', 'PercentFunded')
        plt.title(f'Relationship between LenderYield, PercentFunded and {col}',
                 fontdict = {'weight': 'bold'}, pad = pad)
    else:
        sb.scatterplot(x = 'LenderYield', y = 'PercentFunded', data = not_including_1,\
                       hue = col, s = 70);
    if not facet:
        plt.title(f'Relationship between LenderYield, PercentFunded and {col}',
                 fontdict = {'weight': 'bold'}, pad  = 10)
        plt.xlabel('LenderYield', fontdict = {'weight':'bold'})
        plt.ylabel('PercentFunded', fontdict = {'weight':'bold'});
    plt.legend(loc = 'right', bbox_to_anchor = (1.5,1))


vari(col = 'EmploymentStatus')


vari('LoanStatus', facet = True, pad = 650)

No handles with labels found to put in legend.

<Figure size 720x360 with 0 Axes>


vari('ProsperRating (Alpha)')


vari('CurrentlyInGroup')


vari(col = 'LoanOriginationQuarter', facet= True, pad = 250)

No handles with labels found to put in legend.

<Figure size 720x360 with 0 Axes>


vari(col = 'IncomeVerifiable')


vari(numerical= True, col= 'TotalCreditLinespast7years')


vari(numerical= True, col= 'CreditScoreRangeLower')


vari(numerical= True, col = 'CreditScoreRangeUpper', minimum = 19)


vari(numerical= True, col = 'MonthlyLoanPayment')

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
0	1021339766868145413AB3B	193129	2007-08-26 19:09:29.263000000	C	36	Completed	2009-08-14 00:00:00	0.16516	0.1580	0.1380	NaN	NaN	NaN	NaN	NaN	NaN	0	CO	Other	Self-employed	2.0	True	True	NaN	2007-08-26 18:41:46.780000000	640.0	659.0	2001-10-11 00:00:00	5.0	4.0	12.0	1	24.0	3.0	3.0	2.0	472.0	4.0	0.0	0.0	0.0	0.00	1500.0	11.0	0.81	0.0	0.17	$25,000-49,999	True	3083.333333	E33A3400205839220442E84	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	78	19141	9425	2007-09-12 00:00:00	Q3 2007	1F3E3376408759268057EDA	330.43	11396.1400	9425.00	1971.1400	-133.18	0.0	0.0	0.0	0.0	1.0	0	0	0.0	258
1	10273602499503308B223C1	1209647	2014-02-27 08:28:07.900000000	NaN	36	Current	NaN	0.12016	0.0920	0.0820	0.07960	0.0249	0.05470	6.0	A	7.0	2	CO	Professional	Employed	44.0	False	False	NaN	2014-02-27 08:28:14	680.0	699.0	1996-03-18 00:00:00	14.0	14.0	29.0	13	389.0	3.0	5.0	0.0	0.0	0.0	1.0	0.0	3989.0	0.21	10266.0	29.0	1.00	2.0	0.18	$50,000-74,999	True	6125.000000	9E3B37071505919926B1D82	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	0	134815	10000	2014-03-03 00:00:00	Q1 2014	1D13370546739025387B2F4	318.93	0.0000	0.00	0.0000	0.00	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1
2	0EE9337825851032864889A	81716	2007-01-05 15:00:47.090000000	HR	36	Completed	2009-12-17 00:00:00	0.28269	0.2750	0.2400	NaN	NaN	NaN	NaN	NaN	NaN	0	GA	Other	Not available	NaN	False	True	783C3371218786870A73D20	2007-01-02 14:09:10.060000000	480.0	499.0	2002-07-27 00:00:00	NaN	NaN	3.0	0	0.0	0.0	1.0	1.0	NaN	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.06	Not displayed	True	2083.333333	6954337960046817851BCB2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	86	6466	3001	2007-01-17 00:00:00	Q1 2007	5F7033715035555618FA612	123.32	4186.6300	3001.00	1185.6300	-24.20	0.0	0.0	0.0	0.0	1.0	0	0	0.0	41
3	0EF5356002482715299901A	658116	2012-10-22 11:02:35.010000000	NaN	36	Current	NaN	0.12528	0.0974	0.0874	0.08490	0.0249	0.06000	6.0	A	9.0	16	GA	Skilled Labor	Employed	113.0	True	False	NaN	2012-10-22 11:02:32	800.0	819.0	1983-02-28 00:00:00	5.0	5.0	29.0	7	115.0	0.0	1.0	4.0	10056.0	14.0	0.0	0.0	1444.0	0.04	30754.0	26.0	0.76	0.0	0.15	$25,000-49,999	True	2875.000000	A0393664465886295619C51	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	16	77296	10000	2012-11-01 00:00:00	Q4 2012	9ADE356069835475068C6D2	321.45	5143.2000	4091.09	1052.1100	-108.01	0.0	0.0	0.0	0.0	1.0	0	0	0.0	158
4	0F023589499656230C5E3E2	909464	2013-09-14 18:38:39.097000000	NaN	36	Current	NaN	0.24614	0.2085	0.1985	0.18316	0.0925	0.09066	3.0	D	4.0	2	MN	Executive	Employed	44.0	True	False	NaN	2013-09-14 18:38:44	680.0	699.0	2004-02-20 00:00:00	19.0	19.0	49.0	6	220.0	1.0	9.0	0.0	0.0	0.0	0.0	0.0	6193.0	0.81	695.0	39.0	0.95	2.0	0.26	$100,000+	True	9583.333333	A180369302188889200689E	1.0	11.0	11.0	0.0	0.0	11000.0	9947.90	NaN	0	NaN	6	102670	15000	2013-09-20 00:00:00	Q3 2013	36CE356043264555721F06C	563.97	2819.8500	1563.22	1256.6300	-60.27	0.0	0.0	0.0	0.0	1.0	0	0	0.0	20
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113932	E6D9357655724827169606C	753087	2013-04-14 05:55:02.663000000	NaN	36	Current	NaN	0.22354	0.1864	0.1764	0.16490	0.0699	0.09500	4.0	C	5.0	1	IL	Food Service Management	Employed	246.0	True	False	NaN	2013-04-14 05:54:58	700.0	719.0	1997-09-01 00:00:00	9.0	9.0	41.0	9	209.0	0.0	0.0	0.0	0.0	7.0	1.0	0.0	7714.0	0.80	1886.0	37.0	0.83	3.0	0.13	$50,000-74,999	True	4333.333333	9BD7367919051593140DB62	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	11	88485	10000	2013-04-22 00:00:00	Q2 2013	2EC435768441332602FDC15	364.74	3647.4000	2238.38	1409.0200	-75.58	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1
113933	E6DB353036033497292EE43	537216	2011-11-03 20:42:55.333000000	NaN	36	FinalPaymentInProgress	NaN	0.13220	0.1110	0.1010	0.10070	0.0200	0.08070	6.0	A	8.0	7	PA	Professional	Employed	21.0	True	False	NaN	2011-11-03 20:42:53	700.0	719.0	1992-01-17 00:00:00	14.0	13.0	39.0	9	495.0	1.0	4.0	1.0	5062.0	4.0	0.0	0.0	15743.0	0.69	6658.0	39.0	0.92	0.0	0.11	$75,000-99,999	True	8041.666667	62D93634569816897D5A276	3.0	60.0	60.0	0.0	0.0	33501.0	4815.42	-26.0	0	NaN	28	55801	2000	2011-11-07 00:00:00	Q4 2011	55C4336679182766893E4FC	65.57	2330.5500	1997.16	333.3900	-30.05	0.0	0.0	0.0	0.0	1.0	0	0	0.0	22
113934	E6E13596170052029692BB1	1069178	2013-12-13 05:49:12.703000000	NaN	60	Current	NaN	0.23984	0.2150	0.2050	0.18828	0.1025	0.08578	3.0	D	3.0	1	TX	Other	Employed	84.0	True	False	NaN	2013-12-13 05:49:15	700.0	719.0	2002-02-25 00:00:00	12.0	12.0	25.0	9	521.0	1.0	2.0	0.0	0.0	0.0	0.0	0.0	22147.0	0.73	7853.0	25.0	1.00	0.0	0.51	$25,000-49,999	True	2875.000000	DD1A370200396006300ACA0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	3	123122	10000	2013-12-23 00:00:00	Q4 2013	0FE0370029359765342FDB5	273.35	546.7000	183.15	363.5500	-16.91	0.0	0.0	0.0	0.0	1.0	0	0	0.0	119
113935	E6EB3531504622671970D9E	539056	2011-11-14 13:18:26.597000000	NaN	60	Completed	2013-08-13 00:00:00	0.28408	0.2605	0.2505	0.24450	0.0850	0.15950	4.0	C	5.0	2	GA	Food Service	Full-time	94.0	True	False	NaN	2011-11-14 13:18:24	680.0	699.0	1993-12-01 00:00:00	11.0	11.0	22.0	7	488.0	1.0	4.0	0.0	0.0	0.0	1.0	0.0	11956.0	0.69	4137.0	19.0	0.80	1.0	0.48	$25,000-49,999	True	3875.000000	589536350469116027ED11B	1.0	16.0	16.0	0.0	0.0	5000.0	3264.37	-4.0	0	NaN	28	56401	15000	2011-11-21 00:00:00	Q4 2011	A33834861822272782621C8	449.55	21122.5600	15000.00	6122.5600	-235.05	0.0	0.0	0.0	0.0	1.0	0	0	0.0	274
113936	E6ED3600409833199F711B7	1140093	2014-01-15 09:27:37.657000000	NaN	36	Current	NaN	0.13189	0.1039	0.0939	0.09071	0.0299	0.06081	6.0	A	7.0	1	NY	Professor	Employed	244.0	False	False	NaN	2014-01-15 09:27:40	680.0	699.0	1995-01-01 00:00:00	10.0	9.0	44.0	8	289.0	0.0	1.0	1.0	257.0	3.0	1.0	0.0	6166.0	0.80	675.0	36.0	0.75	0.0	0.23	$50,000-74,999	True	4583.333333	00AF3704550953269A64E40	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	2	127508	2000	2014-01-21 00:00:00	Q1 2014	CE1E3704648000761C9F724	64.90	64.3307	47.25	17.0807	-1.70	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	PercentFunded	Investors
0	1021339766868145413AB3B	193129	2007-08-26 19:09:29.263000000	C	36	Completed	2009-08-14 00:00:00	0.16516	0.1580	0.1380	NaN	NaN	NaN	NaN	NaN	NaN	0	CO	Other	Self-employed	2.0	True	True	NaN	2007-08-26 18:41:46.780000000	640.0	659.0	2001-10-11 00:00:00	5.0	4.0	12.0	1	24.0	3.0	3.0	2.0	472.0	4.0	0.0	0.0	0.0	0.00	1500.0	11.0	0.81	0.0	0.17	$25,000-49,999	True	3083.333333	E33A3400205839220442E84	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	78	19141	9425	2007-09-12 00:00:00	Q3 2007	1F3E3376408759268057EDA	330.43	11396.14	9425.00	1971.14	-133.18	1.0	258
1	10273602499503308B223C1	1209647	2014-02-27 08:28:07.900000000	NaN	36	Current	NaN	0.12016	0.0920	0.0820	0.07960	0.0249	0.05470	6.0	A	7.0	2	CO	Professional	Employed	44.0	False	False	NaN	2014-02-27 08:28:14	680.0	699.0	1996-03-18 00:00:00	14.0	14.0	29.0	13	389.0	3.0	5.0	0.0	0.0	0.0	1.0	0.0	3989.0	0.21	10266.0	29.0	1.00	2.0	0.18	$50,000-74,999	True	6125.000000	9E3B37071505919926B1D82	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	134815	10000	2014-03-03 00:00:00	Q1 2014	1D13370546739025387B2F4	318.93	0.00	0.00	0.00	0.00	1.0	1
2	0EE9337825851032864889A	81716	2007-01-05 15:00:47.090000000	HR	36	Completed	2009-12-17 00:00:00	0.28269	0.2750	0.2400	NaN	NaN	NaN	NaN	NaN	NaN	0	GA	Other	Not available	NaN	False	True	783C3371218786870A73D20	2007-01-02 14:09:10.060000000	480.0	499.0	2002-07-27 00:00:00	NaN	NaN	3.0	0	0.0	0.0	1.0	1.0	NaN	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.06	Not displayed	True	2083.333333	6954337960046817851BCB2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	86	6466	3001	2007-01-17 00:00:00	Q1 2007	5F7033715035555618FA612	123.32	4186.63	3001.00	1185.63	-24.20	1.0	41
3	0EF5356002482715299901A	658116	2012-10-22 11:02:35.010000000	NaN	36	Current	NaN	0.12528	0.0974	0.0874	0.08490	0.0249	0.06000	6.0	A	9.0	16	GA	Skilled Labor	Employed	113.0	True	False	NaN	2012-10-22 11:02:32	800.0	819.0	1983-02-28 00:00:00	5.0	5.0	29.0	7	115.0	0.0	1.0	4.0	10056.0	14.0	0.0	0.0	1444.0	0.04	30754.0	26.0	0.76	0.0	0.15	$25,000-49,999	True	2875.000000	A0393664465886295619C51	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16	77296	10000	2012-11-01 00:00:00	Q4 2012	9ADE356069835475068C6D2	321.45	5143.20	4091.09	1052.11	-108.01	1.0	158
4	0F023589499656230C5E3E2	909464	2013-09-14 18:38:39.097000000	NaN	36	Current	NaN	0.24614	0.2085	0.1985	0.18316	0.0925	0.09066	3.0	D	4.0	2	MN	Executive	Employed	44.0	True	False	NaN	2013-09-14 18:38:44	680.0	699.0	2004-02-20 00:00:00	19.0	19.0	49.0	6	220.0	1.0	9.0	0.0	0.0	0.0	0.0	0.0	6193.0	0.81	695.0	39.0	0.95	2.0	0.26	$100,000+	True	9583.333333	A180369302188889200689E	1.0	11.0	11.0	0.0	0.0	11000.0	9947.9	NaN	NaN	6	102670	15000	2013-09-20 00:00:00	Q3 2013	36CE356043264555721F06C	563.97	2819.85	1563.22	1256.63	-60.27	1.0	20

	ListingNumber	Term	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperScore	ListingCategory (numeric)	EmploymentStatusDuration	CreditScoreRangeLower	CreditScoreRangeUpper	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	StatedMonthlyIncome	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
count	1.139370e+05	113937.000000	113912.000000	113937.000000	113937.000000	84853.000000	84853.000000	84853.000000	84853.000000	84853.000000	113937.000000	106312.000000	113346.000000	113346.000000	106333.000000	106333.000000	113240.000000	113937.00000	113937.000000	113240.000000	112778.000000	113240.000000	106315.000000	112947.000000	113240.000000	106333.000000	1.063330e+05	106333.000000	106393.000000	106393.000000	106393.000000	106393.000000	105383.000000	1.139370e+05	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	18928.000000	113937.000000	16952.000000	113937.000000	113937.000000	113937.00000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000
mean	6.278857e+05	40.830248	0.218828	0.192764	0.182701	0.168661	0.080306	0.096068	4.072243	5.950067	2.774209	96.071582	685.567731	704.567731	10.317192	9.260164	26.754539	6.96979	398.292161	1.435085	5.584405	0.592052	984.507059	4.154984	0.312646	0.015094	1.759871e+04	0.561309	11210.225447	23.230034	0.885897	0.802327	0.275947	5.608026e+03	1.421100	22.934345	22.271949	0.613629	0.048540	8472.311961	2930.313906	-3.223214	152.816539	16.268464	31.896882	69444.474271	8337.01385	272.475783	4183.079489	3105.536588	1077.542901	-54.725641	-14.242698	700.446342	681.420499	25.142686	0.998584	0.048027	0.023460	16.550751	80.475228
std	3.280762e+05	10.436212	0.080364	0.074818	0.074516	0.068467	0.046764	0.030403	1.673227	2.376501	3.996797	94.480605	66.458275	66.458275	5.457866	5.022644	13.637871	4.63097	447.159711	2.437507	6.429946	1.978707	7158.270157	10.160216	0.727868	0.154092	3.293640e+04	0.317918	19818.361309	11.871311	0.148179	1.097637	0.551759	7.478497e+03	0.764042	19.249584	18.830425	2.446827	0.556285	7395.507650	3806.635075	50.063567	466.320254	9.005898	29.974184	38930.479610	6245.80058	192.697812	4790.907234	4069.527670	1183.414168	60.675425	109.232758	2388.513831	2357.167068	275.657937	0.017919	0.332353	0.232412	294.545422	103.239020
min	4.000000e+00	12.000000	0.006530	0.000000	-0.010000	-0.182700	0.004900	-0.182700	1.000000	1.000000	0.000000	0.000000	0.000000	19.000000	0.000000	0.000000	2.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-209.000000	0.000000	0.000000	0.000000	1.000000	1000.00000	0.000000	-2.349900	0.000000	-2.349900	-664.870000	-9274.750000	-94.200000	-954.550000	0.000000	0.700000	0.000000	0.000000	0.000000	1.000000
25%	4.009190e+05	36.000000	0.156290	0.134000	0.124200	0.115670	0.042400	0.074080	3.000000	4.000000	1.000000	26.000000	660.000000	679.000000	7.000000	6.000000	17.000000	4.00000	114.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.121000e+03	0.310000	880.000000	15.000000	0.820000	0.000000	0.140000	3.200333e+03	1.000000	9.000000	9.000000	0.000000	0.000000	3500.000000	0.000000	-35.000000	0.000000	9.000000	6.000000	37332.000000	4000.00000	131.620000	1005.760000	500.890000	274.870000	-73.180000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	2.000000
50%	6.005540e+05	36.000000	0.209760	0.184000	0.173000	0.161500	0.072400	0.091700	4.000000	6.000000	1.000000	67.000000	680.000000	699.000000	10.000000	9.000000	25.000000	6.00000	271.000000	1.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.549000e+03	0.600000	4100.000000	22.000000	0.940000	0.000000	0.220000	4.666667e+03	1.000000	16.000000	15.000000	0.000000	0.000000	6000.000000	1626.550000	-3.000000	0.000000	14.000000	21.000000	68599.000000	6500.00000	217.740000	2583.830000	1587.500000	700.840100	-34.440000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	44.000000
75%	8.926340e+05	36.000000	0.283810	0.250000	0.240000	0.224300	0.112000	0.116600	5.000000	8.000000	3.000000	137.000000	720.000000	739.000000	13.000000	12.000000	35.000000	9.00000	525.000000	2.000000	7.000000	0.000000	0.000000	3.000000	0.000000	0.000000	1.952100e+04	0.840000	13180.000000	30.000000	1.000000	1.000000	0.320000	6.825000e+03	2.000000	33.000000	32.000000	0.000000	0.000000	11000.000000	4126.720000	25.000000	0.000000	22.000000	65.000000	101901.000000	12000.00000	371.580000	5548.400000	4000.000000	1458.540000	-13.920000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	115.000000
max	1.255725e+06	60.000000	0.512290	0.497500	0.492500	0.319900	0.366000	0.283700	7.000000	11.000000	20.000000	755.000000	880.000000	899.000000	59.000000	54.000000	136.000000	51.00000	14985.000000	105.000000	379.000000	83.000000	463881.000000	99.000000	38.000000	20.000000	1.435667e+06	5.950000	646285.000000	126.000000	1.000000	20.000000	10.010000	1.750003e+06	8.000000	141.000000	141.000000	42.000000	21.000000	72499.000000	23450.950000	286.000000	2704.000000	44.000000	100.000000	136486.000000	35000.00000	2251.510000	40702.390000	35000.000000	15617.030000	32.060000	0.000000	25000.000000	25000.000000	21117.900000	1.012500	39.000000	33.000000	25000.000000	1189.000000

Prosper loan Data Exploration¶

by Adebajo Ruth Oluwadamilola¶

Table of Content¶

Introduction¶

Preliminary Wrangling¶

Importing Python libraries¶

Data Gathering¶

Data Assessing¶

I will assess the data visually and programmatically for quality and tidiness issue.¶

Visual Assessment¶

Programatical Assessment¶

Programmatically assess the loan data¶

Quality Issues¶

Tidiness Issues¶

Data Cleaning¶

Storing Data¶

What is the structure of this dataset?¶

What are the main features of interest in the dataset?¶

What features in the dataset will help support investigation into your features of interest?¶

Exploratory Data Analysis¶

Univarent Exploration¶

Investigating distributions of individual variables¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

Investigating the relationships between pairs of variables of interest in the data.¶

Correlation Analysis¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Conclusions¶

Limitations¶